Search results
Results From The WOW.Com Content Network
Identifiability of the model in the sense of invertibility of the map is equivalent to being able to learn the model's true parameter if the model can be observed indefinitely long. Indeed, if { X t } ⊆ S is the sequence of observations from the model, then by the strong law of large numbers ,
For example, when modeling the flight of an aircraft, we could embed each mechanical part of the aircraft into our model and would thus acquire an almost white-box model of the system. However, the computational cost of adding such a huge amount of detail would effectively inhibit the usage of such a model.
The simplest form of a group-contribution method is the determination of a component property by summing up the group contributions : [] = +.This simple form assumes that the property (normal boiling point in the example) is strictly linearly dependent on the number of groups, and additionally no interaction between groups and molecules are assumed.
In any case, users of a model need to understand the assumptions made that are pertinent to its validity for a given use. Building a model requires abstraction. Assumptions are used in modelling in order to specify the domain of application of the model. For example, the special theory of relativity assumes an inertial frame of reference.
System identification is a method of identifying or measuring the mathematical model of a system from measurements of the system inputs and outputs. The applications of system identification include any system where the inputs and outputs can be measured and include industrial processes, control systems, economic data, biology and the life sciences, medicine, social systems and many more.
Creating a cellular model has been a particularly challenging task of systems biology and mathematical biology. It involves the use of computer simulations of the many cellular subsystems such as the networks of metabolites , enzymes which comprise metabolism and transcription , translation , regulation and induction of gene regulatory networks.
Molecular modelling encompasses all methods, theoretical and computational, used to model or mimic the behaviour of molecules. [1] The methods are used in the fields of computational chemistry, drug design, computational biology and materials science to study molecular systems ranging from small chemical systems to large biological molecules and material assemblies.
Most cellular deconvolution algorithms consider an input data in a form of a matrix , which represents some molecular information (e.g. gene expression data or DNA methylation data) measured over a group of samples and marks (e.g. genes or CpG sites).